Innate Lymphoid Cells and Natural Killer Cells in Bacterial Infections: Function, Dysregulation, and Therapeutic Targets
Bibliographic record
Abstract
Infectious diseases represent one of the largest medical challenges worldwide. Bacterial infections, in particular, remain a pertinent health challenge and burden. Moreover, such infections increase over time due to the continuous use of various antibiotics without medical need, thus leading to several side effects and bacterial resistance. Our innate immune system represents our first line of defense against any foreign pathogens. This system comprises the innate lymphoid cells (ILCs), including natural killer (NK) cells that are critical players in establishing homeostasis and immunity against infections. ILCs are a group of functionally heterogenous but potent innate immune effector cells that constitute tissue-resident sentinels against intracellular and extracellular bacterial infections. Being a nascent subset of innate lymphocytes, their role in bacterial infections is not clearly understood. Furthermore, these pathogens have developed methods to evade the host immune system, and hence permit infection spread and tissue damage. In this review, we highlight the role of the different ILC populations in various bacterial infections and the possible ways of immune evasion. Additionally, potential immunotherapies to manipulate ILC responses will be briefly discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".